Retrieval-Augmented Generation (RAG)

A RAG pipeline has three stages. First, retrieval: the user's question is converted into search queries, often several via query fan-out, and run against an index to fetch candidate documents. Second, ranking: candidates are filtered and ordered, frequently with a reranking model scoring true relevance. Third, generation: top passages are inserted into the model's context, and the LLM composes an answer grounded in them, usually with citations.

The model never reads the whole web at answer time, only the handful of passages that survive retrieval and ranking. Everything else effectively does not exist for that answer.

Every cited AI answer, in Perplexity, ChatGPT Search, Google's AI surfaces, Copilot, is a RAG output, and your page earns a citation only by winning all three stages: being indexed and retrieved, surviving the rerank, and containing a passage the model actually uses. Each stage has its own failure mode: blocked crawlers kill retrieval, weak relevance kills ranking, and vague prose kills passage selection.

This is why RAG-era optimization is passage-level work. A 3,000-word page competes as individual chunks; the chunk either supports a claim cleanly or gets passed over.

Write sections that stand alone: a descriptive heading, a direct answer in the first sentence, supporting facts after. Keep crawlers unblocked, freshness signals current, and key claims stated as quotable sentences rather than buried in narrative. Structured data and clean HTML help retrieval systems parse you correctly.

You can verify the payoff empirically: when content works, it starts appearing as a source. Geonimo's citation tracking records which of your URLs each engine cites for your tracked prompts, closing the loop between content changes and RAG outcomes.